What the data for this category looks like
Data for construction and decoration investment research comes primarily from public bidding announcements, construction process logs, material supplier quotation libraries, national and industry construction standard documents, and project settlement ledgers. Update frequency fluctuates with project progress. Updates to project-related documents range from daily to monthly. Most individual documents include fields such as project number, construction area, material category, price range, construction period milestones, and acceptance specifications. Units include square meters, cubic meters, yuan per square meter, workdays, and others.
What constraints these characteristics impose on context and token workflows
The text length of construction and decoration investment research data varies widely. A single bidding announcement can span hundreds to thousands of words, leading to significant fluctuations in token usage per context. Structured fields in material quotation libraries and project settlement ledgers can cause excessive token consumption during batch recall. Project-related data requires associating multiple document fragments to form a complete investment research context, further increasing token usage. Unrestricted recall of frequently updated project documents will quickly exhaust token quotas. Geographically differentiated field priorities also require targeted recall of locally relevant documents, adding token costs for context splicing.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the average token usage of a single core document for construction and decoration, avoiding excessive single context token consumption |
chunkSize | 1000–1500 characters | Matches the structured paragraph length of construction and decoration documents, reducing context breaks after segmentation |
recallCount | Top 6–8 results | Balances multi-dimensional data coverage required for investment research and token consumption, avoiding excessive recall that leads to token overrun |
similarityThreshold | 0.75–0.85 | Filters low-relevance construction and decoration data, reducing invalid token usage |
rerankReturnCount | Top 4–5 results | Performs secondary screening on recall results, retaining highly relevant fragments and optimizing token usage efficiency |
tokenLimitPerChat | 10% below the model's supported maximum | Reserves redundant capacity to handle token consumption from multi-document association for construction and decoration projects |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: When the value of
recallCountormaxContextis set too high, the chat interface displays an "exceeded context token" error, or the large language model does not return valid results. Cause: Multi-document associated recall for construction and decoration projects exceeds the token limit supported by the model, with insufficient tokens reserved for prompt and reply content. - Symptom: Segmented document fragments show field breaks in the context preview, such as material price and category split into different fragments. Cause: The
chunkSizevalue is too small, splitting structured construction and decoration document fields into meaningless segments. - Symptom: After importing an ultra-long project milestone data structure, the large language model cannot parse the complete content. Cause:
tokenLimitPerChatis not adjusted to adapt to ultra-long text, or ultra-long data is not split into compliant token ranges via workflows.
How to confirm correct configuration
- Upload a typical construction and decoration bidding announcement, check that the segmented sections in the context preview retain complete structured fields with no obvious breaks.
- Initiate an investment research query, verify that the number of returned context fragments matches the configured values of
recallCountandrerankReturnCount. - Import ultra-long project settlement ledger data, observe that the large language model can fully parse associated fields with no truncation prompts.
- Adjust the
maxContextvalue, verify that context token usage matches expected values across different settings with no overrun errors.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.